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Activity Number:
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376
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Type:
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Contributed
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Date/Time:
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Wednesday, August 1, 2007 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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| Abstract - #309628 |
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Title:
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Binary Time Series Modeling with Application to Kinetic
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Author(s):
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Ying Hung*+ and Chien-Fu Jeff Wu
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Companies:
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Georgia Institute of Technology and Georgia Institute of Technology
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Address:
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1460 F willow lake dr, Atlanta, GA, 30329,
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Keywords:
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binary time series ; goodness-of-fit
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Abstract:
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Micropipette experimentation is a new biotechnological method developed to measure the kinetic rates of cell adhesion which play an important role in tumor metastasis and cancer mutation. Traditional analysis of micropipette experiments assumes that the adhesion test cycles are independent Bernoulli trials. This assumption can often be violated in practice. In this paper, a multiple time series model incorporating random effects is developed to analyze the repeated adhesion tests. A goodness-of-fit statistic is introduced to assess the adequacy of distribution assumptions on the dependent binary data with random effects. The asymptotic distribution of the goodness-of-fit statistic is derived. Application of the proposed methodology to some real data in an T-cell micropipette experiment reveals some interesting information on the dependency between repeated adhesion tests.
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